from woodwork import logical_types
from .binary_classification_pipeline import BinaryClassificationPipeline
from .multiclass_classification_pipeline import (
MulticlassClassificationPipeline,
)
from .pipeline_base import PipelineBase
from .regression_pipeline import RegressionPipeline
from .time_series_classification_pipelines import (
TimeSeriesBinaryClassificationPipeline,
TimeSeriesMulticlassClassificationPipeline,
)
from .time_series_regression_pipeline import TimeSeriesRegressionPipeline
from evalml.data_checks import DataCheckActionCode, TargetDistributionDataCheck
from evalml.model_family import ModelFamily
from evalml.pipelines.components import ( # noqa: F401
CatBoostClassifier,
CatBoostRegressor,
ComponentBase,
DateTimeFeaturizer,
DelayedFeatureTransformer,
DropColumns,
DropNullColumns,
EmailFeaturizer,
Estimator,
Imputer,
LogTransformer,
OneHotEncoder,
RandomForestClassifier,
SklearnStackedEnsembleClassifier,
SklearnStackedEnsembleRegressor,
SMOTENCOversampler,
SMOTENOversampler,
SMOTEOversampler,
StandardScaler,
TargetImputer,
TextFeaturizer,
Undersampler,
URLFeaturizer,
)
from evalml.pipelines.components.utils import get_estimators
from evalml.problem_types import (
ProblemTypes,
handle_problem_types,
is_classification,
is_regression,
is_time_series,
)
from evalml.utils import get_logger, import_or_raise, infer_feature_types
logger = get_logger(__file__)
def _get_preprocessing_components(
X, y, problem_type, estimator_class, sampler_name=None
):
"""Given input data, target data and an estimator class, construct a recommended preprocessing chain to be combined with the estimator and trained on the provided data.
Arguments:
X (pd.DataFrame): The input data of shape [n_samples, n_features]
y (pd.Series): The target data of length [n_samples]
problem_type (ProblemTypes or str): Problem type
estimator_class (class): A class which subclasses Estimator estimator for pipeline,
sampler_name (str): The name of the sampler component to add to the pipeline. Defaults to None
Returns:
list[Transformer]: A list of applicable preprocessing components to use with the estimator
"""
pp_components = []
if is_regression(problem_type):
for each_action in TargetDistributionDataCheck().validate(X, y)["actions"]:
if each_action["metadata"]["transformation_strategy"] == "lognormal":
pp_components.append(LogTransformer)
all_null_cols = X.columns[X.isnull().all()]
if len(all_null_cols) > 0:
pp_components.append(DropNullColumns)
index_and_unknown_columns = list(
X.ww.select(["index", "unknown"], return_schema=True).columns
)
if len(index_and_unknown_columns) > 0:
pp_components.append(DropColumns)
email_columns = list(X.ww.select("EmailAddress", return_schema=True).columns)
if len(email_columns) > 0:
pp_components.append(EmailFeaturizer)
url_columns = list(X.ww.select("URL", return_schema=True).columns)
if len(url_columns) > 0:
pp_components.append(URLFeaturizer)
input_logical_types = {type(lt) for lt in X.ww.logical_types.values()}
types_imputer_handles = {
logical_types.Boolean,
logical_types.Categorical,
logical_types.Double,
logical_types.Integer,
logical_types.URL,
logical_types.EmailAddress,
}
text_columns = list(X.ww.select("NaturalLanguage", return_schema=True).columns)
if len(text_columns) > 0:
pp_components.append(TextFeaturizer)
if len(input_logical_types.intersection(types_imputer_handles)) or len(
text_columns
):
pp_components.append(Imputer)
datetime_cols = list(X.ww.select(["Datetime"], return_schema=True).columns)
add_datetime_featurizer = len(datetime_cols) > 0
if add_datetime_featurizer and estimator_class.model_family != ModelFamily.ARIMA:
pp_components.append(DateTimeFeaturizer)
if (
is_time_series(problem_type)
and estimator_class.model_family != ModelFamily.ARIMA
):
pp_components.append(DelayedFeatureTransformer)
# The URL and EmailAddress Featurizers will create categorical columns
categorical_cols = list(
X.ww.select(["category", "URL", "EmailAddress"], return_schema=True).columns
)
if len(categorical_cols) > 0 and estimator_class not in {
CatBoostClassifier,
CatBoostRegressor,
}:
pp_components.append(OneHotEncoder)
sampler_components = {
"Undersampler": Undersampler,
"SMOTE Oversampler": SMOTEOversampler,
"SMOTENC Oversampler": SMOTENCOversampler,
"SMOTEN Oversampler": SMOTENOversampler,
}
if sampler_name is not None:
try:
import_or_raise(
"imblearn.over_sampling", error_msg="imbalanced-learn is not installed"
)
pp_components.append(sampler_components[sampler_name])
except ImportError:
logger.debug(
f"Could not import imblearn.over_sampling, so defaulting to use Undersampler"
)
pp_components.append(Undersampler)
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
pp_components.append(StandardScaler)
return pp_components
def _get_pipeline_base_class(problem_type):
"""Returns pipeline base class for problem_type"""
if problem_type == ProblemTypes.BINARY:
return BinaryClassificationPipeline
elif problem_type == ProblemTypes.MULTICLASS:
return MulticlassClassificationPipeline
elif problem_type == ProblemTypes.REGRESSION:
return RegressionPipeline
elif problem_type == ProblemTypes.TIME_SERIES_REGRESSION:
return TimeSeriesRegressionPipeline
elif problem_type == ProblemTypes.TIME_SERIES_BINARY:
return TimeSeriesBinaryClassificationPipeline
else:
return TimeSeriesMulticlassClassificationPipeline
[docs]def make_pipeline(
X,
y,
estimator,
problem_type,
parameters=None,
sampler_name=None,
extra_components=None,
):
"""Given input data, target data, an estimator class and the problem type,
generates a pipeline class with a preprocessing chain which was recommended based on the inputs.
The pipeline will be a subclass of the appropriate pipeline base class for the specified problem_type.
Arguments:
X (pd.DataFrame): The input data of shape [n_samples, n_features]
y (pd.Series): The target data of length [n_samples]
estimator (Estimator): Estimator for pipeline
problem_type (ProblemTypes or str): Problem type for pipeline to generate
parameters (dict): Dictionary with component names as keys and dictionary of that component's parameters as values.
An empty dictionary or None implies using all default values for component parameters.
sampler_name (str): The name of the sampler component to add to the pipeline. Only used in classification problems.
Defaults to None
extra_components (list(ComponentBase)): List of extra components to be added after preprocessing components. Defaults to None.
Returns:
PipelineBase object: PipelineBase instance with dynamically generated preprocessing components and specified estimator
"""
X = infer_feature_types(X)
y = infer_feature_types(y)
problem_type = handle_problem_types(problem_type)
if estimator not in get_estimators(problem_type):
raise ValueError(f"{estimator.name} is not a valid estimator for problem type")
if not is_classification(problem_type) and sampler_name is not None:
raise ValueError(
f"Sampling is unsupported for problem_type {str(problem_type)}"
)
preprocessing_components = _get_preprocessing_components(
X, y, problem_type, estimator, sampler_name
)
extra_components = extra_components or []
complete_component_list = preprocessing_components + extra_components + [estimator]
component_graph = PipelineBase._make_component_dict_from_component_list(
complete_component_list
)
base_class = _get_pipeline_base_class(problem_type)
return base_class(
component_graph,
parameters=parameters,
)
[docs]def generate_pipeline_code(element):
"""Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
Arguments:
element (pipeline instance): The instance of the pipeline to generate string Python code
Returns:
String representation of Python code that can be run separately in order to recreate the pipeline instance.
Does not include code for custom component implementation.
"""
# hold the imports needed and add code to end
code_strings = []
if not isinstance(element, PipelineBase):
raise ValueError(
"Element must be a pipeline instance, received {}".format(type(element))
)
if isinstance(element.component_graph, dict):
raise ValueError("Code generation for nonlinear pipelines is not supported yet")
code_strings.append(
"from {} import {}".format(
element.__class__.__module__, element.__class__.__name__
)
)
code_strings.append(repr(element))
return "\n".join(code_strings)
def _make_stacked_ensemble_pipeline(
input_pipelines, problem_type, n_jobs=-1, random_seed=0
):
"""
Creates a pipeline with a stacked ensemble estimator.
Arguments:
input_pipelines (list(PipelineBase or subclass obj)): List of pipeline instances to use as the base estimators for the stacked ensemble.
This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
problem_type (ProblemType): problem type of pipeline
n_jobs (int or None): Integer describing level of parallelism used for pipelines.
None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
Defaults to -1.
Returns:
Pipeline with appropriate stacked ensemble estimator.
"""
parameters = {}
if is_classification(problem_type):
parameters = {
"Sklearn Stacked Ensemble Classifier": {
"input_pipelines": input_pipelines,
"n_jobs": n_jobs,
}
}
estimator = SklearnStackedEnsembleClassifier
else:
parameters = {
"Sklearn Stacked Ensemble Regressor": {
"input_pipelines": input_pipelines,
"n_jobs": n_jobs,
}
}
estimator = SklearnStackedEnsembleRegressor
pipeline_class, pipeline_name = {
ProblemTypes.BINARY: (
BinaryClassificationPipeline,
"Sklearn Stacked Ensemble Classification Pipeline",
),
ProblemTypes.MULTICLASS: (
MulticlassClassificationPipeline,
"Sklearn Stacked Ensemble Classification Pipeline",
),
ProblemTypes.REGRESSION: (
RegressionPipeline,
"Sklearn Stacked Ensemble Regression Pipeline",
),
}[problem_type]
return pipeline_class(
[estimator],
parameters=parameters,
custom_name=pipeline_name,
random_seed=random_seed,
)
def _make_component_list_from_actions(actions):
"""
Creates a list of components from the input DataCheckAction list
Arguments:
actions (list(DataCheckAction)): List of DataCheckAction objects used to create list of components
Returns:
List of components used to address the input actions
"""
components = []
for action in actions:
if action.action_code == DataCheckActionCode.DROP_COL:
components.append(DropColumns(columns=action.metadata["columns"]))
if action.action_code == DataCheckActionCode.IMPUTE_COL:
metadata = action.metadata
if metadata["is_target"]:
components.append(
TargetImputer(impute_strategy=metadata["impute_strategy"])
)
return components